There are techniques known, for instance from Japanese Unexamined Patent Application Publication No. 2010-258914, which use image analytics to automatically detect regions in an image likely to visually attract a person's attention, or that are abnormal (hereafter, these kinds of regions are referred to as “visual attention regions”). These kinds of techniques are referred to as visual attention detection, or saliency detection, or the like, and have been gaining lots of attention as important key features in the field of computer vision. Visual attention detection for video in particular, is expected to have application in various fields; for instance, using surveillance cameras to detect unusual or abnormal circumstances, or in the automatic operation of vehicles or robots.
Algorithms for visual attention detection can be largely divided into model-based techniques or learning-based techniques. In a model-based technique, image features that should be determined as abnormal are presented as the model, and the regions having these kinds of image features are extracted from an image. However, positing unknown abnormal states is non-trivial, and it tends to be extremely difficult to implement a model capable of supporting a variety of real world events. In contrast, a learning-based technique uses a large quantity of training data to learn the image features that should be determined as normal or abnormal. The advantage is that the learning-based method can provide a simpler way of building a highly accurate detector without requiring models or hypotheses. Unfortunately, this method is highly dependent on the training data; thus, the detection accuracy deteriorates when the training data is unsuitable. There are also cases where over time the subject being monitored, the situation, and the environment changes, and the knowledge gained through training becomes unsuitable even when the detector is trained in advance with suitable training data. In such a cases, new training data corresponding to the current situation must be prepared and used to retrain the detector, and maintenance of the detector is a hassle.